Randomized Algorithms for Non-Intrusive Parametric Reduced Order Modeling

This paper demonstrates the development of purely data-driven, nonintrusive parametric reduced-order models for the emulation of high-dimensional field outputs using randomized linear algebra techniques. Typically, low-dimensional representations are built using the proper orthogonal decomposition c...

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Veröffentlicht in:	AIAA journal 2020-12, Vol.58 (12), p.5389-5407
Hauptverfasser:	Rajaram, Dushhyanth, Perron, Christian, Puranik, Tejas G, Mavris, Dimitri N
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Computational efficiency Computational fluid dynamics Computing costs Decomposition Fluid flow Interpolation Linear algebra Model accuracy Proper Orthogonal Decomposition Reduced order models Singular value decomposition
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